Current medicinal chemistry最新文献

Traditional organic solvents often pose environmental and toxicity concerns in the synthesis of active pharmaceutical ingredients (APIs), the cornerstone of pharmaceutical drugs. Deep eutectic solvents (DESs), characterized by their versatility and efficiency as both solvents and catalysts, offer a promising alternative for sustainable drug synthesis. The dual capacity of DESs as green solvents and catalysts holds significant potential for enhancing the sustainability and efficiency of drug-synthesis processes. This study comprehensively explores the synthesis of various drug scaffolds, including those relevant to central nervous system (CNS) disorders, inflammation, cancer, and other therapeutic areas. By examining reaction mechanisms and parameters, the research provides valuable insights into the high yields achievable using DESs. The review also highlights the effectiveness of different types of DESs in drug synthesis, including natural DESs (NADESs), reactive DESs (RDESs), water-based DESs (WDESs), and ionic liquid-based DESs (ILDESs). Among these, NADESs are the most commonly used, with choline chloride (ChCl)-based DESs standing out as the most popular, utilized in over [30] different combinations mentioned in the review. The most frequently used ChCl-based DES was ChCl/urea, followed by ChCl/taurine. The collected data provide important information, including optimal DES combinations, ratios, concentrations, and reaction conditions for producing drug scaffolds with the highest yields. The numerous synthetic results presented in this article demonstrate that widespread adoption of DESs in both research and industrial settings could have a significant positive environmental impact, owing to their low toxicity, renewability, affordability, and energy-efficient catalytic properties. This review offers a thorough exploration of the use of DESs in drug synthesis. By analyzing key chemical equations, reaction procedures, reaction mechanisms, yields, and critical parameters from reported studies, this report aims to present a valuable resource to guide researchers in optimizing synthetic strategies and advancing the application of DESs in pharmaceutical chemistry.

Introduction: Cancer-associated fibroblasts (CAFs) play vital roles in HCC initiation and progression via multicellular, stromal-dependent processes. Despite their acknowledged significance, the comprehensive role of CAF-related gene signatures in HCC remains underexplored. Given the established role of CAFs in HCC progression and therapy resistance, we aimed to develop a CAF-derived gene signature for prognosis prediction.

Methods: This comprehensive analysis used RNA sequencing data from The Cancer Genome Atlas database and a validation cohort from the International Cancer Genome Consortium database. Differential gene expression analysis identified CAF-related genes associated with HCC prognosis. A gene signature was developed using the least absolute shrinkage and selection operator algorithm and Cox regression analysis, and its prognostic value was evaluated through Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC) analysis. The signature's associations with immune cell infiltration, chemotherapeutic response, and functional enrichment were investigated to further evaluate the prognostic value of CAF-related genes.

Results: In total, 256 differentially expressed CAF-related genes were identified between samples with high and low CAF infiltration. After systematic analysis, a prognostic signature, including 23 genes, was constructed. Kaplan-Meier and ROC analyses demonstrated the robust predictive potential of our risk model for patients with HCC. Univariate/ multivariate Cox regression analyses further confirmed that the risk model was an independent prognostic factor for HCC in both the testing and validation cohorts. The signature stratified patients into high- and low-risk groups with distinct survival (logrank p < 0.001), achieving areas under the curve of 0.848 (1 year), 0.795 (3 years), and 0.781 (5 years). Patients in the high-risk group were more responsive to vinblastine, docetaxel, and navitoclax. Patients in the low-risk group had greater chemosensitivity to olaparib. Moreover, the stromal score was significantly higher in the high-risk group. Finally, the signaling pathways enriched in the high-risk group were mainly associated with HCC progression.

Discussion: We established a CAF-related gene signature model as an independent prognostic predictor for HCC. This model may also guide adjuvant chemotherapy and targeted therapy, though further validation across diverse cohorts is needed to confirm its clinical utility.

Conclusion: The CAF-related gene signature exhibited good predictive potential concerning the prognosis of patients with HCC.

Background: Cardiac remodeling Post-Myocardial Infarction (MI) drives heart failure. Geniposide (GP), a traditional Chinese medicine-derived compound, exhibits cardioprotective potential, yet its mechanisms remain unclear. This study explored the GP's role in post-MI remodeling via Parkin-dependent mitophagy.

Methods: Murine MI and cardiomyocyte Chronic Hypoxia (CH) models were established. MI mice received GP; cardiac function, histopathology, apoptosis, fibrosis/autophagy markers, and mitochondrial clearance were assessed. in vitro, Parkin-silenced hypoxic cardiomyocytes were used to evaluate GP's effects on viability, oxidative stress, mitochondrial function, autophagy proteins, and autophagosome formation.

Results: in vivo, GP improved cardiac function, reduced fibrosis/apoptosis, and suppressed fibrosis-related genes (Col1a1, Col3a1, Tgfb1, Mmp9). GP enhanced clearance of damaged mitochondria via autophagy, mitigating oxidative stress. in vitro, GP's protection against hypoxia required Parkin: it preserved mitochondrial homeostasis, inhibited ROS-mediated apoptosis, and reduced autophagosome accumulation. Mechanistically, GP attenuated excessive mitophagy by modulating Parkin, thereby maintaining mitochondrial quality and reducing oxidative injury.

Conclusion: GP alleviates post-MI remodeling by suppressing Parkin-dependent hyperactivated mitophagy, reducing cardiomyocyte loss and fibrosis. Parkin is central to GP's therapeutic effects, highlighting its potential as a target for MI-related heart failure. This study elucidates GP's cardioprotective mechanism and proposes Parkin pathway modulation as a novel strategy to counteract pathological cardiac remodeling.

Quercetin, a naturally occurring flavonol found abundantly in fruits, vegetables, and medicinal plants, has gained considerable attention for its diverse pharmacological properties and therapeutic potential in cancer treatment. Preclinical evidence highlights its multifaceted biological activities, including antioxidant, anti-inflammatory, pro-apoptotic, and anti-angiogenic effects, which collectively target several hallmarks of cancer. Quercetin exerts its anticancer effects by modulating key molecular pathways such as PI3K/Akt, MAPK, NF-κB, and Nrf2, thereby influencing cell proliferation, apoptosis, metastasis, and regulation of the tumor microenvironment. Despite promising laboratory and animal model data, its poor solubility, low bioavailability, and rapid metabolism limit its clinical application. Drug delivery systems such as nanoparticle formulations, liposomes, and micelles have shown potential to overcome these barriers and enhance therapeutic efficacy. Moreover, combination therapies of quercetin with conventional chemotherapeutics demonstrate synergistic effects, offering improved efficacy and reduced toxicity. While early clinical trials and supportive-care applications suggest a favorable safety profile, concerns regarding pharmacokinetics, drug-nutrient interactions, and dose-related side effects remain unresolved. This review summarizes the current knowledge of quercetin's molecular mechanisms, delivery strategies, safety considerations, and clinical prospects, and provides perspectives for future research aimed at optimizing its role as an adjuvant or standalone anticancer agent.

Introduction: This study explored potential atrial fibrillation (AF) risk genes via nicotinamide adenine dinucleotide (NAD+) metabolism using tissue samples and the Gene Expression Omnibus (GEO) database.

Methods: A cross-sectional study was conducted on atrial tissues from patients undergoing left atrial appendage resection. Whole transcriptome sequencing was performed on 3 AF and 3 control samples. The GSE115574 and GSE79768 datasets were analyzed, yielding 51 NMRGs. DE-mRNAs were screened and overlapped to obtain DE-NMRGs. LASSO and RFE algorithms identified critical genes, and enrichment and drug-prediction analyses were conducted. Gene expressions were validated in 5 additional patients using qRT-PCR.

Results: Three key genes (SLC6A6, ATP1B4, and BEX2) were identified, associated with energy metabolism pathways and potentially influencing AF progression through immune response modulation.

Discussion: Previous studies reported the role of NAD+ metabolism in AF, but its mechanism is unclear. This study identified SLC6A6, ATP1B4, and BEX2 as gene signatures linking NAD+ metabolism to AF. These genes are involved in metabolic or electrophysiological processes that can predispose to arrhythmogenesis. However, their specific mechanisms of action remain unclear, and further research is needed.

Conclusion: The study identified three key genes (SLC6A6, ATP1B4, and BEX2) involved in NAD+ metabolism, with diagnostic potential for AF patients and associations with energy metabolism and immune infiltration.

Introduction: Telomeres have become extensively studied in renal cell carcinoma (RCC), and this study aims to identify relevant diagnostic biomarkers in the predominant RCC subtype, clear cell RCC (ccRCC).

Materials and methods: This study retrieved telomere-related genes from the TelNet database and integrated data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to calculate telomere enrichment scores using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were then applied to identify candidate genes, which were further refined through protein-protein interaction (PPI) network construction and two machine learning methods: least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE). The associations between the identified feature genes and immune cell infiltration were subsequently evaluated using CIBERSORT and ESTIMATE. Furthermore, single-cell analysis was employed to determine the highly expressed genes in different cell clusters. Finally, using a ccRCC cell line, quantitative real-time PCR, wound healing, and Transwell assays were performed to validate the expression and potential biological functions of the selected key genes.

Results: A higher telomere score was observed in ccRCC. The common genes from the DEGs and the gene modules were mainly enriched in cell division- and senescence-related pathways. Moreover, six genes (ASPM, CENPF, CEP55, MELK, BUB1, and EXO1) were identified as feature genes with satisfactory diagnostic efficacy and high expression in ccRCC; they were positively correlated with most immune cells and highly expressed in T cells. Notably, CEP55 knockdown suppressed the migration and invasion of ccRCC cells.

Discussion: Our present study, based on the data from the public databases, unraveled 6 genes with diagnostic efficacy in ccRCC, which may aid the development of a relevant future diagnostic method in ccRCC.

Conclusion: This study identified six telomere-related genes with high expression and strong diagnostic value in ccRCC, highlighting their association with immune infiltration and potential as diagnostic and therapeutic targets.

Background: Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis and a high fatality rate. To predict the prognosis of HNSCC, this study developed a prognostic model based on nitrogen metabolism (NM)-related genes.

Methods: This study utilized transcriptomic data and clinical information from HNSCC obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify differentially expressed NM-related genes. Subsequently, an NMrelated prognostic risk model was established by integrating univariate Cox regression, LASSO regression, and multivariate Cox regression. Its predictive value was validated using Kaplan-Meier and ROC curves. Further analysis using GSVA and CIBERSORT examined the relationship between the risk model and the tumor microenvironment immune status, while also evaluating chemotherapy drug sensitivity across different risk groups. Finally, protein-protein interaction (PPI) networks and key gene screening were employed, and the functional validation of the core genes was conducted through in vitro experiments.

Results: We identified 10 key NM-related genes (GLS, ASNS, EXT2, HPRT1, SLC7A5, SMS, B3GNT8, GATM, NAGK, and SULT1B1) to construct a prognostic risk model. The GSVA analysis revealed that the low-risk group was enriched in immune-related pathways, while the high-risk group favored metabolic pathways. Additionally, the low-risk group exhibited higher levels of immune cell infiltration. We discovered that gefitinib, belinostat, erlotinib, and phenformin were more effective against cancer cells with lower risk scores. The PPI network screening identified key hub genes, including LORICRIN. Experimental validation demonstrated that LORICRIN overexpression significantly suppressed migration and invasion in HNSCC cells, suggesting its potential tumor- suppressive role in carcinogenesis and progression.

Discussion: This study emphasizes the links between NM signatures, immune regulation, and signaling pathways, underscoring their potential in the HNSCC mechanism research.

Conclusion: Our study established a NM-related gene signature closely linked to immune microenvironment and drug sensitivity, highlighting potential biomarkers and therapeutic targets for prognosis and personalized therapy in HNSCC.

Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition associated with high mortality and morbidity. However, targeted therapies that effectively improve patient outcomes remain limited. Exosomes play pivotal roles in intercellular communication and epigenetic regulation.

Objective: This study aimed to identify exosome-related differentially expressed genes (EXORDEGs) in whole blood associated with ARDS and to explore their potential mechanistic roles in the disease.

Methods: Two gene expression datasets (GSE32707 and GSE66890) were retrieved from the Gene Expression Omnibus for comprehensive bioinformatics analysis. Analytical approaches included Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, protein-protein interaction network construction using the STRING database, and immune infiltration profiling via single-sample gene set enrichment analysis in relation to hub genes.

Results: We identified 21 EXORDEGs, primarily enriched in biological processes such as endothelial cell development and apoptosis. Four hub genes-PI3, EEF1A1, ANAPC1, and PSMD2-were robustly associated with ARDS, with PSMD2 showing the most pronounced differential expression. Immune infiltration analysis revealed significant disparities in nine immune cell populations between ARDS and control samples.

Discussion: The results of this comprehensive bioinformatics analysis identified four EXORDEGs-PI3, EEF1A1, ANAPC1, and PSMD2-with important roles in acute respiratory distress syndrome.

Conclusion: This study first systematically identified EXORDEGs in ARDS, discovering four hub genes and their associations with immune cells. The hub genes may be implicated in endothelial injury, inflammation, and immune dysregulation. These findings provide novel insights into ARDS pathogenesis and highlight potential therapeutic targets for further investigation. Given the disease heterogeneity, our findings primarily reflect common molecular characteristics, while the specific features of different etiological subtypes require further investigation.

Introduction: Non-Small Cell Lung Cancer (NSCLC) treatment is often challenged by drug resistance. The antihypertensive drug telmisartan has shown anti-tumor potential, but its underlying mechanism remains unclear. Ferroptosis, a newly identified form of cell death, may serve as a promising therapeutic target. The objective is to investigate whether telmisartan inhibits NSCLC by inducing ferroptosis and to elucidate its underlying mechanism.

Methods: in vitro cell assays and in vivo mouse models were used, along with molecular biology techniques, to evaluate the effects of telmisartan on NSCLC and its mechanism of action.

Results: Telmisartan significantly suppressed NSCLC cell proliferation and tumor growth. Mechanistic studies revealed that telmisartan induced ferroptosis by inhibiting the nuclear translocation of Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) and downregulating Glutathione Peroxidase 4 (GPX4) expression. The anti-tumor effect of telmisartan was reversed by ferroptosis inhibitors.

Discussion: Telmisartan can inhibit the proliferation of NSCLC cells in vitro and in vivo and induce cell ferroptosis. Telmisartan can also inhibit the nuclear translocation of NRF2, thereby affecting the expression of GPX4.

Conclusion: Telmisartan inhibited NSCLC by inducing ferroptosis via the NRF2/GPX4 axis, offering a new therapeutic strategy and potential clinical application for NSCLC treatment.

Background: Observational studies have suggested associations between circulating metabolites and breast cancer (BC) risk, but the direction and causality of these relationships remain unclear due to confounding and reverse causation. Therefore, we aimed to evaluate the potential causal effects of 1,400 circulating metabolites on BC subtypes using Mendelian randomization (MR) based on GWAS data from European-ancestry populations.

Methods: Two-sample and reverse MR analyses were performed to explore potential causal links between metabolites and BC from the FinnGen and Breast Cancer Association Consortium (BCAC) cohorts. The inverse-variance weighted (IVW) approach served as the main analytical method to evaluate these associations. To further ensure the robustness and credibility of the MR findings, sensitivity analyses were conducted, incorporating leave-one-out procedures, the Cochran's Q test, and the MR-Egger intercept test.

Results: Following correction using the False Discovery Rate (FDR) method at a significance level of 0.10, we identified 5alpha-pregnan-3beta,20alpha-diol monosulfate levels (p = 6.7714*10-5, PFDR = 0.0798) and Myristoleate (14:1n5) levels (p =0.0002, PFDR = 0.0798) were associated with an increased risk of ER+ BC. Conversely, the Caffeine to paraxanthine ratio (p =0.0001, PFDR = 0.0798) was associated with a reduced risk. In the reverse MR analysis, interactions were observed between Eicosanedioate (C20-DC) levels, Piperine levels, Caffeine to theobromine ratio, Indolepropionate levels, 1-oleoyl- GPC (18:1) levels, and Oleoylcarnitine levels with BC. Notably, the p-values of intercept terms in MR-Egger regression all exceeded 0.05, suggesting an absence of potential horizontal pleiotropy.

Discussion: These findings suggested that hormone-related, lipid-related, and diet- derived metabolites might play subtype-specific roles in breast cancer development. The identified metabolites provided mechanistic insights and highlighted potential biological pathways that warrant further functional validation. They may also serve as preliminary biomarkers for future metabolomic and translational research.

Conclusion: Our MR study identified several metabolites that may be causally associated with BC risk. These findings offer potential candidates for further mechanistic investigation and highlight the importance of subtype-specific approaches in metabolomics research.